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Alexander Michaelides - One of the best experts on this subject based on the ideXlab platform.
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optimal life cycle Asset Allocation understanding the empirical evidence
Journal of Finance, 2005Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We show that a life-cycle model with realistically calibrated uninsurable labor income risk and moderate risk aversion can simultaneously match stock market participation rates and Asset Allocation decisions conditional on participation. The key ingredients of the model are Epstein–Zin preferences, a fixed stock market entry cost, and moderate heterogeneity in risk aversion. Households with low risk aversion smooth earnings shocks with a small buffer stock of Assets, and consequently most of them (optimally) never invest in equities. Therefore, the marginal stockholders are (endogenously) more risk averse, and as a result they do not invest their portfolios fully in stocks.
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optimal life cycle Asset Allocation understanding the empirical evidence
Social Science Research Network, 2003Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We show that a life-cycle model with realistically calibrated uninsurable labor income risk and moderate risk aversion can simultaneously match stock market participation rates and Asset Allocation decisions conditional on participation. The key ingredients of the model are Epstein-Zin preferences, two risky Assets (stocks and long-term bonds), and a fixed entry cost associated with the investment in risky Assets. In this context, moderate preference heterogeneity in risk aversion and in the elasticity of intertemporal substitution is sufficient to deliver our results. Moreover, the model rationalizes the Asset Allocation puzzle of Canner, Mankiw and Weil (1997).
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life cycle Asset Allocation a model with borrowing constraints uninsurable labor income risk and stock market participation costs
2002Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We study life-cycle Asset Allocation in the presence of liquidity constraints and undiversifiable labor income risk. The model includes three different Assets (cash, long-term government bonds and stocks) and it takes into account the life-cycle profile of housing expenditures. With a modest correlation between stock returns and earnings innovations, the mean share of wealth invested in stocks never exceeds 45% during working-life. Moreover, the combination of uninsurable human capital and borrowing constraints rationalizes the Asset Allocation puzzle of Canner, Mankiw and Weil (1997). Nevertheless we argue that Asset Allocation models must match another important feature of the data: a low stock market participation rate. Along this dimension the model provides a very modest improvement, still predicting a counterfactually high participation rate. We show that this arises from the link between risk aversion and prudence, implying that explanations for the participation puzzle based on the role of background risk are unlikely to succeed.
Francisco Gomes - One of the best experts on this subject based on the ideXlab platform.
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optimal life cycle Asset Allocation understanding the empirical evidence
Journal of Finance, 2005Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We show that a life-cycle model with realistically calibrated uninsurable labor income risk and moderate risk aversion can simultaneously match stock market participation rates and Asset Allocation decisions conditional on participation. The key ingredients of the model are Epstein–Zin preferences, a fixed stock market entry cost, and moderate heterogeneity in risk aversion. Households with low risk aversion smooth earnings shocks with a small buffer stock of Assets, and consequently most of them (optimally) never invest in equities. Therefore, the marginal stockholders are (endogenously) more risk averse, and as a result they do not invest their portfolios fully in stocks.
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optimal life cycle Asset Allocation understanding the empirical evidence
Social Science Research Network, 2003Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We show that a life-cycle model with realistically calibrated uninsurable labor income risk and moderate risk aversion can simultaneously match stock market participation rates and Asset Allocation decisions conditional on participation. The key ingredients of the model are Epstein-Zin preferences, two risky Assets (stocks and long-term bonds), and a fixed entry cost associated with the investment in risky Assets. In this context, moderate preference heterogeneity in risk aversion and in the elasticity of intertemporal substitution is sufficient to deliver our results. Moreover, the model rationalizes the Asset Allocation puzzle of Canner, Mankiw and Weil (1997).
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life cycle Asset Allocation a model with borrowing constraints uninsurable labor income risk and stock market participation costs
2002Co-Authors: Francisco Gomes, Alexander MichaelidesAbstract:We study life-cycle Asset Allocation in the presence of liquidity constraints and undiversifiable labor income risk. The model includes three different Assets (cash, long-term government bonds and stocks) and it takes into account the life-cycle profile of housing expenditures. With a modest correlation between stock returns and earnings innovations, the mean share of wealth invested in stocks never exceeds 45% during working-life. Moreover, the combination of uninsurable human capital and borrowing constraints rationalizes the Asset Allocation puzzle of Canner, Mankiw and Weil (1997). Nevertheless we argue that Asset Allocation models must match another important feature of the data: a low stock market participation rate. Along this dimension the model provides a very modest improvement, still predicting a counterfactually high participation rate. We show that this arises from the link between risk aversion and prudence, implying that explanations for the participation puzzle based on the role of background risk are unlikely to succeed.
Thomas Raffinot - One of the best experts on this subject based on the ideXlab platform.
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hierarchical clustering based Asset Allocation
Social Science Research Network, 2017Co-Authors: Thomas RaffinotAbstract:A hierarchical clustering based Asset Allocation method, which uses graph theory and machine learning techniques, is proposed. Classical and more modern hierarchical clustering methods are tested, such as Simple Linkage or Directed Bubble Hierarchical Tree for example. Once the Assets are hierarchically clustered, a simple and efficient capital Allocation within and across clusters of Assets at multiple hierarchical levels is computed. The out-of-sample performances of hierarchical clustering based portfolios and more traditional risk-based portfolios are evaluated across three disparate datasets. To avoid data snooping, the comparison of profit measures is assessed using the bootstrap based model confidence set procedure. The empirical results indicate that hierarchical clustering based portfolios are robust, truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.
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Hierarchical Clustering-Based Asset Allocation
SSRN Electronic Journal, 2016Co-Authors: Thomas RaffinotAbstract:This article proposes a hierarchical clustering-based Asset Allocation method, which uses graph theory and machine learning techniques. Hierarchical clustering refers to the formation of a recursive clustering, suggested by the data, not defined a priori. Several hierarchical clustering methods are presented and tested. Once the Assets are hierarchically clustered, the authors compute a simple and efficient capital Allocation within and across clusters of Assets, so that many correlated Assets receive the same total Allocation as a single uncorrelated one. The out-of-sample performances of hierarchical clustering-based portfolios and more traditional risk-based portfolios are evaluated across three disparate datasets, which differ in term of the number of Assets and the Assets’ composition. To avoid data snooping, the authors assess the comparison of profit measures using the bootstrap-based model confidence set procedure. Their empirical results indicate that hierarchical clustering-based portfolios are robust and truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.
Allan Timmermann - One of the best experts on this subject based on the ideXlab platform.
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Asset Allocation under multivariate regime switching
Social Science Research Network, 2006Co-Authors: Massimo Guidolin, Allan TimmermannAbstract:This paper studies Asset Allocation decisions in the presence of regime switching in Asset returns. We find evidence that four separate regimes - characterized as crash, slow growth, bull and recovery states - are required to capture the joint distribution of stock and bond returns. Optimal Asset Allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal Allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of Asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Welfare costs from ignoring regime switching can be substantial even after accounting for parameter uncertainty. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in Asset returns.
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Asset Allocation under multivariate regime switching
Journal of Economic Dynamics and Control, 2005Co-Authors: Massimo Guidolin, Allan TimmermannAbstract:This paper studies Asset Allocation decisions in the presence of regime switching in Asset returns. We find evidence that four separate regimes - characterized as crash, slow growth, bull and recovery states - are required to capture the joint distribution of stock and bond returns. Optimal Asset Allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal Allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of Asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Welfare costs from ignoring regime switching can be substantial even after accounting for parameter uncertainty. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in Asset returns. ; Earlier title: Strategic Asset Allocation and consumption decisions under multivariate regime switching
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international Asset Allocation under regime switching skew and kurtosis preferences
Review of Financial Studies, 2005Co-Authors: Massimo Guidolin, Allan TimmermannAbstract:This paper proposes a new tractable approach to solving Asset Allocation problems in situations with a large number of risky Assets which pose problems for standard approaches. Investor preferences are assumed to be defined over moments of the wealth distribution such as its mean, variance, skew and kurtosis. Time-variations in investment opportunities are represented by a flexible regime switching process. In the context of a four-moment international CAPM specification that relates stock returns in five regions to returns on a global market portfolio, we find evidence of distinct bull and bear states. Ignoring regimes, an unhedged US investor’s optimal portfolio is strongly diversified internationally. The presence of regimes in the return distribution leads to a large increase in the investor’s optimal holdings of US stocks as does the introduction of skew and kurtosis preferences. Our paper therefore offers an explanation of the strong home bias observed in US investors’ Asset Allocation based on regime switching and skew and kurtosis preferences.
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Asset Allocation under multivariate regime switching
Journal of Economic Dynamics and Control, 2005Co-Authors: Massimo Guidolin, Allan TimmermannAbstract:Abstract This paper studies Asset Allocation decisions in the presence of regime switching in Asset returns. We find evidence that four separate regimes – characterized as crash, slow growth, bull and recovery states – are required to capture the joint distribution of stock and bond returns. Optimal Asset Allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal Allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of Asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in Asset returns.
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Asset Allocation dynamics and pension fund performance
The Journal of Business, 1999Co-Authors: David Blake, Bruce N Lehmann, Allan TimmermannAbstract:Using a data set on more than 300 U.K. pension funds' Asset holdings, this article provides a systematic investigation of the performance of managed portfolios across multiple Asset classes. We find evidence of slow mean reversion in the funds' portfolio weights toward a common, time-varying strategic Asset Allocation. We also find surprisingly little cross-sectional variation in the average ex post returns arising from the strategic-Asset-Allocation, market-timing, and security-selection decisions of the fund managers. Strategic Asset Allocation accounts for most of the time-series variation in portfolio returns, while market timing and Asset selection appear to have been far less important. Copyright 1999 by University of Chicago Press.
Omid Shakernia - One of the best experts on this subject based on the ideXlab platform.
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risk parity portfolio vs other Asset Allocation heuristic portfolios
The Journal of Investing, 2011Co-Authors: Denis B Chaves, Jason C Hsu, Omid ShakerniaAbstract:In this article, the authors conduct a horse race between representative risk parity portfolios and other Asset Allocation strategies, including equal weighting, minimum variance, mean–variance optimization, and the classic 60/40 equity/ bond portfolio. They find that the traditional risk parity portfolio construction does not consistently outperform (in terms of risk-adjusted return) equal weighting or a model pension fund portfolio anchored to the 60/40 equity/bond portfolio structure. However, it does significantly outperform such optimized Allocation strategies as minimum variance and mean–variance efficient portfolios. Over the last 30 years, the Sharpe ratios of the risk parity and the equal-weighting portfolios have been much more stable across decade-long subperiods than either the 60/40 portfolio or the optimized portfolios. Although risk parity performs on par with equal weighting, it does provide better diversification in terms of risk Allocation and thus warrants further consideration as an Asset Allocation strategy. The authors show, however, that the performance of the risk parity strategy can be highly dependent on the investment universe. Thus, to execute risk parity successfully, the careful selection of Asset classes is critical, which, for the time being, remains an art rather than a formulaic exercise based on theory.
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risk parity portfolio vs other Asset Allocation heuristic portfolios
Social Science Research Network, 2010Co-Authors: Denis B Chaves, Jason C Hsu, Omid ShakerniaAbstract:In this paper, we conduct a horse race between representative Risk Parity portfolios and other Asset Allocation strategies, including equal weighting, minimum-variance, mean-variance optimization, and the classic 60/40 equity/bond portfolio. We find that the traditional Risk Parity portfolio construction does not consistently outperform on a risk-adjusted basis the equal weighting or a model pension fund portfolio anchored to the 60/40 equity/bond portfolio structure. However, it does significantly outperform optimized Allocation strategies such as minimum-variance and mean-variance efficient portfolios on a consistent basis. Over the past 30 years, the Sharpe Ratios of the Risk Parity and the equal weighting portfolio have been much more stable across decade-long sub-periods than either the 60/40 portfolio or the optimized portfolios. Although Risk Parity performs on par with equal weighting, it does provide better diversification in terms of risk Allocation and, thus, warrants further consideration as an Asset Allocation strategy. However, we show that the Risk Parity strategy’s performance can be highly dependent on the investment universe. Thus, to execute on Risk Parity successfully, careful selection of Asset classes is very critical and, for the time being, remains an art rather than a science.
